Sensing and Estimation of Fire-Induced Wind Field Using Small UAS

Wildland fire may greatly affect the nearby and surrounding wind fields, by creating strong inflows, updrafts, downdrafts, and vortices. It may create challenging operating conditions for manned and unmanned aircraft. This paper presents a method to sense and estimate fire-induced wind fields using small fixed-wing UAS equipped with standard avionics, including a conventional pitot-tube. A novel model-aided extended Kalman filter (EKF) is developed to simultaneously estimate the air-relative velocity components, 3D wind velocities, and corresponding wind accelerations along the flight trajectory using onboard sensor measurements and the UAS dynamic model. The estimated wind velocity and acceleration can be used to identify gust parameters (e.g., updraft velocity, gust period, horizontal wind shear), characterize plume structure, and support data-driven modeling of fire-generated updrafts and downdrafts. The performance of the estimation filter is first validated using simulation data over one-minus-cosine type updraft field. The filter is then further validated using a prescribed fire dataset where a KHawk Zephyr3 UAS was flown near and through fire-generated plumes at an altitude of about 115 meters in auto waypoint tracking mode. During UAS plume encounters, the estimated maximum updraft velocities range from 4.13–8.6 m/s, with a gust period of 5.8–9.1 s. The wind acceleration within the updraft region ranges from -12.1–14.2 m/s^2.